Google relies on user proximity to provide local results for keywords. How vital is the proximity factor? How fast does the ranking decrease by distance from the location of a business?
The study’s goals are to estimate the drop in the ranking by geographical distance and to measure the variability due to the local context (city).
For this study, we focused on personal injury lawyers in major US cities. We collected 20 top-ranking personal injury lawyers in each of the 50 largest cities.
For each of these law firms, we used the service Local Falcon to collect Google My Business rankings for listings that show up either in the Maps portion of the organic search or from a search in the Google Maps Local Finder (i.e. Google Maps).
We collected their rankings for the keyword car accident lawyer at 225 locations on a 15x15 grid centered on their geographic location.
This is an example for the city of Miami:
At the location of the law firm, it ranks 1st for the keyword car accident lawyer. Its ranking drops, however, as soon we are further away from its location. At the fringe of the grid, the law firm does not appear anymore in the top 20 (its exact ranking is not tracked anymore by Local Falcon).
This drop in the ranking can vary drastically between law firms, even in the same city. We see this variation if we flank our initial example with two other samples from Miami:
On the left, we see a very rapid drop in the ranking. On the right, we witness the case of a law firm’s ranking that does not drop much. The grid is always centered on the location of the target law firm.
To account for this high variation between the firms, we need to gather several samples in each city; we collected 20. We used a radius of 10 miles. This allows us to highlight the drop in ranking around the firm’s exact location and identify the distance where most firms drop out of the top 20. Furthermore, for the ten largest cities, we also collected ten samples at a 5 miles radius, a finer granularity, to better highlight the drop in ranking around the firm’s location.
After collecting the data, we can reproduce the grid shown above with a heat map. Below is for instance the same law firm. Each tile is the rank of the law firm observed by Local Falcon on the 15x15 grid centered at the firm’s location. The grid measures 10 miles by 10 miles.
Then, we can visualize on the same 10-mile by 10-mile grid all of the 20 law firm samples collected in Miami (the ten first samples were collected on a 5-mile by 5-mile grid and are not shown below). Sample 12 is the one shown above. We observe that the law firm of sample 11 keeps ranking high even at a high distance, whereas sample law firm 13 directly drops out of the top 20 outside its location.
The grids for all 50 cities are shown in the Annex. The data for this study can be found here.
Most of the 1100 law firms rank 1st at their own location (56%).
We want to compute the ranking by distance to a law firm’s location. So, we compute the geographical distance to the location of the target law firm from the latitude and longitude of each of the 225 measurements on the 15x15 grid. We then average the ranking of a law firm by mile distance to its own location.
There is a major caveat of the data collected with Local Falcon: Local Falcon does not collect rankings above 20 - the first page of search results; they are just collected as “20+”. So, to numerically estimate the decline in ranking, for instance by computing the average rank at a certain distance from a law firm’s localization, we need to impute the value of these missing ranks. For the sake of this study, we assigned the value of 25 to all “20+” measurements. While this is not perfect and impacts the computation of the average ranking, it still allows us to visualize this decline.
For instance, with our previous example in Miami, we see that the law firm ranked first at its own location (distance = 0 miles). The ranking drops quickly, and the position of all the measurements taken between 0 and 1 miles averages to ~9. The average rank oscillates then around 20 as from beginning mile 3. The further away from the location, the more often the firm’s ranking is high or out of the top 20. We used indeed the value of 25 for “+20”, reflected in the average. The average is in orange when above 20, i.e., where law firms rank mostly out of the top 20.
To obtain more stable measurements of the drop in ranking, we average the rankings from each law firm, which why we collected 20 samples per city.
We start by visualizing the rank at each mile from the center location for each law firm in each city. Each line is a sample - a law firm.
First, for the most populated and less populated city:
Then, for all 50 largest US cities:
We observe that the patterns are slightly different between cities. There is nevertheless a consistency: the drop in ranking varies greatly between law firms. Some law firms only see a slight drop in their ranking, even at 5 or 10 miles from their location. Other law firms quickly drop out of the top 20 (showed in orange on the plot).
Because there is high variability between the law firms, it is helpful to show the average rank at each mile to highlight the general trend:
And for all 50 cities: